Distributed photovoltaic group regulation and control algorithm, computer equipment and computer storage medium

Abstract

The invention discloses a distributed photovoltaic group regulation and control algorithm, computer equipment and a computer readable storage medium. The regulation and control algorithm comprises the following steps: determining a target function according to a regulation and control target, target power and a reference voltage level of the distributed photovoltaic group; determining constraint conditions, wherein the constraint conditions comprise the output power constraint of the current photovoltaic power generation unit, the output constraint of the current photovoltaic power generation unit and the power flow constraint; determining the control weight of the current photovoltaic power generation unit according to the constraint condition and the target function; and controlling the output power of the current photovoltaic power generation unit according to the control weight so as to regulate and control the distributed photovoltaic group. According to the control method, the output power constraint, the photovoltaic power generation unit output constraint and the power flow constraint are added when the objective function is solved, and the environmental information is quantified, so that the target power solved according to the constraint conditions can optimize the generated power and keep the voltage stable.

Description

Technical Field

[0001] The present application relates to the field of distributed photovoltaic technology, and more specifically, to a control algorithm for a distributed photovoltaic group, a computer device, and a computer-readable storage medium. Background Art

[0002] At present, the regulation of distributed photovoltaic groups is mainly carried out through planning methods, taking into account multiple conditions such as the topological structure of the power grid, power flow distribution, and voltage stability, and constructing a distributed photovoltaic group regulation model by constructing objective functions and constraints. Deep reinforcement learning is also applied to the regulation of distributed photovoltaic groups. By analyzing environmental information and extracting features from it, the environmental changes are abstractly perceived, and a reinforcement learning model is constructed to achieve the purpose of optimizing the target regulation of distributed photovoltaic groups.

[0003] In related technologies, when constructing an enhanced model, it is necessary to consider environmental information such as topology, flow calculation, voltage stability, and reactive power optimization. However, it is difficult to quantify environmental information, resulting in poor results in the constructed enhanced model. Summary of the invention

[0004] Embodiments of the present application provide a control algorithm for a distributed photovoltaic group, a computer device, and a computer-readable storage medium.

[0005] The embodiment of the present application provides a control algorithm for a distributed photovoltaic group, wherein the distributed photovoltaic group includes a plurality of photovoltaic power generation units arranged in a distributed manner, and the control algorithm includes:

[0006] Determining an objective function according to a control target, a target power and a reference voltage level of the distributed photovoltaic group;

[0007] Determining constraint conditions, wherein the constraint conditions include an output power constraint of a current photovoltaic power generation unit, an output constraint of a current photovoltaic power generation unit, and a power flow constraint;

[0008] Determining a control weight of the current photovoltaic power generation unit according to the constraint condition and the objective function;

[0009] The output power of the current photovoltaic power generation unit is controlled according to the control weight to achieve regulation of the distributed photovoltaic group.

[0010] In this way, the control algorithm, computer device, and computer-readable storage medium of the distributed photovoltaic group of the implementation mode of the present application can obtain the control weight by determining the objective function according to the control target and determining the constraint conditions including the power flow constraint, and then controlling the output of the photovoltaic power generation unit according to the control weight, thereby realizing the group control of the distributed photovoltaic group to achieve the control target. By controlling the individual photovoltaic power generation units, the group control algorithm is not restricted by the topology of the distributed photovoltaic group, and the versatility is improved. In addition, the output power constraint, the photovoltaic power generation unit output constraint, and the power flow constraint are added, and the environmental information is quantified, so that the target power obtained by solving the constraint conditions can optimize the power generation power and maintain voltage stability.

[0011] In some embodiments, determining the objective function according to the regulation target, target power and reference voltage level of the distributed photovoltaic group includes:

[0012] Determine the power loss according to the total output power value of the distributed photovoltaic group and the target power, the total output power value is the sum of the output power values ​​of all the photovoltaic power generation units;

[0013] determining a voltage loss according to an actual voltage level of the photovoltaic power generation unit and a reference voltage level;

[0014] The objective function is determined according to the power loss and the voltage loss.

[0015] In this way, the power loss is determined according to the total output power values ​​of the photovoltaic power generation units and the target power, and the voltage loss is determined according to the actual voltage level of the photovoltaic power generation units and the reference voltage level. The objective function is then determined according to the power loss and voltage loss. This allows the objective function to meet the control objectives, that is, to control the total output power value to be close to the target power and to control the voltage to be stable.

[0016] In some embodiments, determining the objective function according to the control target, target power and reference voltage level of the distributed photovoltaic group further includes:

[0017] If the photovoltaic power generation unit is under rigid control, determining an output power value of the photovoltaic power generation unit as a product of a first control weight and an output power prediction value, wherein the first control weight is equal to a first preset value or a second preset value;

[0018] If the photovoltaic power generation unit belongs to flexible control, the output power value of the photovoltaic power generation unit is determined to be the product of a second control weight and an output power prediction value, and the second control weight is greater than the first preset value and less than the second preset value.

[0019] In this way, the output power of the photovoltaic power generation unit at the current moment can be determined according to the control weight and power prediction value of the photovoltaic power generation unit.

[0020] In some embodiments, determining the objective function according to the power loss and the voltage loss includes:

[0021] The objective function is determined according to a sum of the square of the power loss and the square of the voltage loss.

[0022] In this way, the objective function can be determined based on the power loss and voltage loss, so that the control weights obtained by solving the objective function can meet the requirements of the regulation target.

[0023] In some embodiments, determining the constraints includes:

[0024] The output power constraint is determined according to the predicted power generation power of the current photovoltaic power generation unit, and the output power constraint includes: the output power of the current photovoltaic power generation unit is less than the predicted power generation power.

[0025] In this way, by setting the power constraint, the obtained control weight can meet the demand for power stability.

[0026] In some embodiments, determining the constraints includes:

[0027] The current photovoltaic power generation unit output constraint is determined according to the voltage upper limit value and the voltage lower limit value, and the current photovoltaic power generation unit output constraint includes: the voltage of the current photovoltaic power generation unit is greater than the voltage lower limit value and less than the voltage upper limit value.

[0028] In this way, by setting the output constraint, the voltage of the current photovoltaic power generation unit can be controlled to be greater than the voltage lower limit value and less than the voltage upper limit value, so as to maintain the voltage between the voltage upper limit value and the voltage lower limit value.

[0029] In some embodiments, determining the constraints comprises:

[0030] The power flow constraint is determined according to Kirchhoff's voltage-current law and the system active and reactive power balance formula.

[0031] In this way, the power flow constraint can be determined according to Kirchhoff's voltage-current law and the system active and reactive power balance formula so that the distributed photovoltaic group meets the power flow balance requirements.

[0032] In some embodiments, the power flow constraint includes an active power flow constraint, the distributed photovoltaic group includes a first node and a second node, and the determining constraint condition includes:

[0033] The active power flow constraint is determined according to the active power flow between the first node and the second node of the distributed photovoltaic group, the reactive power flow between the first node and the second node, the voltage value of the photovoltaic power generation unit at the first node, the resistance value between the first node and the second node, and the active power injected into the second node.

[0034] In this way, active power balance at the node can be ensured by determining active power flow constraints.

[0035] In some embodiments, the power flow constraint includes a reactive power flow constraint, the distributed photovoltaic group includes a third node and a fourth node, and the determining constraint condition includes:

[0036] The reactive power flow constraint is determined according to the active power flow between the third node and the fourth node of the distributed photovoltaic group, the reactive power flow between the third node and the fourth node, the voltage value of the photovoltaic power generation unit at the third node, the reactance value between the third node and the fourth node, and the active power injected by the fourth node.

[0037] In this way, reactive power balance at the node can be ensured by determining reactive power flow constraints.

[0038] In some embodiments, the power flow constraint includes a power balance power flow constraint, the distributed photovoltaic group includes a fifth node and a sixth node, and the determining constraint condition includes:

[0039] The power balance flow constraint is determined according to the voltage value of the photovoltaic power generation unit at the fifth node, the voltage value of the photovoltaic power generation unit at the sixth node, the resistance value between the fifth node and the sixth node, the active flow between the fifth node and the sixth node, the reactive flow between the fifth node and the sixth node, and the reactance and impedance between the fifth node and the sixth node.

[0040] In this way, by determining the active power flow constraint, it is possible to ensure that the power balance relationship can be maintained during the power transmission process.

[0041] In some embodiments, determining the control weight of the current photovoltaic power generation unit according to the constraint condition and the objective function includes:

[0042] The control weight is determined according to the constraint conditions and the objective function using a particle swarm algorithm.

[0043] In this way, the particle swarm algorithm is used to solve the objective function according to the constraints, and the control weight can be quickly obtained.

[0044] An embodiment of the present application provides a computer device, which includes one or more processors and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the control algorithm as described in any of the above embodiments are implemented.

[0045] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the control algorithm of any of the above embodiments are implemented.

[0046] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0048] Figure 1 It is a schematic flow chart of the control method of the embodiment of the present application;

[0049] Figure 2 is a schematic diagram of a distributed photovoltaic group according to an embodiment of the present application;

[0050] Figure 3 It is a schematic flow chart of the control method of the embodiment of the present application;

[0051] Figure 4 It is a schematic flow chart of the control method of the embodiment of the present application;

[0052] Figure 5 It is a schematic flow chart of the control method of the embodiment of the present application;

[0053] Figure 6 It is a schematic flow chart of the control method of the embodiment of the present application;

[0054] Figure 7 It is a schematic flow chart of the control method of the embodiment of the present application;

[0055] Figure 8 It is a schematic flow chart of the control method of the embodiment of the present application;

[0056] Fig. 9 It is a schematic flow chart of the control method of the embodiment of the present application;

[0057] Fig.10 is a schematic diagram of a branch of a distributed photovoltaic group according to an implementation mode of the present application;

[0058] Fig.11It is a schematic flow chart of the control method of the embodiment of the present application;

[0059] Fig.12 It is a schematic flow chart of the control method of the embodiment of the present application;

[0060] Fig.13 It is a schematic flow chart of the control method according to the embodiment of the present application. DETAILED DESCRIPTION

[0061] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0062] With the rapid development of distributed photovoltaics, due to the access of a large number of distributed photovoltaic groups in low-voltage areas, the distribution network has problems such as reverse power supply to transformer overload and user overvoltage, resulting in the inability of distributed photovoltaic groups to be connected to the grid in some areas.

[0063] At present, the regulation of distributed photovoltaic groups is mainly carried out through planning methods, taking into account multiple conditions such as the topological structure of the power grid, power flow distribution, and voltage stability, and constructing a distributed photovoltaic group regulation model by constructing objective functions and constraints. Deep reinforcement learning is also applied to the regulation of distributed photovoltaic groups. By analyzing environmental information and extracting features from it, the environmental changes are abstractly perceived, and a reinforcement learning model is constructed to achieve the purpose of optimizing the target regulation of distributed photovoltaic groups.

[0064] In related technologies, when constructing an enhanced model, it is necessary to consider environmental information such as topology, flow calculation, voltage stability, and reactive power optimization. However, it is difficult to quantify environmental information, resulting in poor results in the constructed enhanced model.

[0065] Based on the above possible problems, please refer to Figure 1 and Figure 2 The embodiment of the present application provides a control algorithm for a distributed photovoltaic group 100, wherein the distributed photovoltaic group 100 includes a plurality of photovoltaic power generation units 10 arranged in a distributed manner, and the control algorithm includes:

[0066] 01: Determine the objective function according to the control target, target power and reference voltage level of the distributed photovoltaic group 100;

[0067] 02: Determine the constraint conditions, which include the output power constraint of the current photovoltaic power generation unit 10, the output constraint of the current photovoltaic power generation unit 10 and the power flow constraint;

[0068] 03: Determine the control weight of the current photovoltaic power generation unit 10 according to the constraint conditions and the objective function;

[0069] 04: Control the output power of the current photovoltaic power generation unit 10 according to the control weight to achieve regulation of the distributed photovoltaic group 100.

[0070] The embodiment of the present application provides a computer device, which includes one or more processors and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the control algorithm as in any of the above embodiments are implemented. That is, the processor can be used to: determine the objective function according to the control target, target power and reference voltage level of the distributed photovoltaic group 100; and to determine the constraint conditions, which include the output power constraint of the current photovoltaic power generation unit 10, the output constraint of the current photovoltaic power generation unit 10 and the flow constraint; and to determine the control weight of the current photovoltaic power generation unit 10 according to the constraint conditions and the objective function; and can also be used to control the output power of the current photovoltaic power generation unit 10 according to the control weight, so as to achieve the control of the distributed photovoltaic group 100.

[0071] The embodiment of the present application provides a control device, which includes a first determination module, a second determination module, a third determination module and a control module. Among them, the first determination module can be used to determine the objective function according to the control target, target power and reference voltage level of the distributed photovoltaic group 100; the second determination module can be used to determine the constraint conditions, which include the output power constraint of the current photovoltaic power generation unit 10, the output constraint of the current photovoltaic power generation unit 10 and the flow constraint; the third determination module can be used to determine the control weight of the current photovoltaic power generation unit 10 according to the constraint conditions and the objective function; the control module can be used to control the output power of the current photovoltaic power generation unit 10 according to the control weight, so as to realize the control of the distributed photovoltaic group 100.

[0072] Specifically, see Figure 2 The distributed photovoltaic group 100 is connected to the distribution network, the distribution network includes multiple nodes, and the distributed photovoltaic group 100 includes multiple photovoltaic power generation units 10 (DG) arranged in a distributed manner, and the photovoltaic power generation units 10 are distributed on multiple nodes. By controlling each single photovoltaic power generation unit 10, the overall output power of the distributed photovoltaic group 100 can be controlled.

[0073] The control objectives include adjusting the difference between the distributed generation power and the target power to be less than the set error and maintaining voltage stability. Among them, the target power is the preset control target value of the output power, the set error is the maximum acceptable error between the preset output power and the target power, and the reference voltage level is a preset voltage value, which can be set according to actual needs and are not limited here.

[0074] Determine the objective function according to the control target, target power and reference voltage level, then determine the constraints, and solve the objective function according to the constraints to determine the control weight w of the current photovoltaic power generation unit m (t). The constraints include the output power constraint of the current photovoltaic power generation unit, the output constraint of the current photovoltaic power generation unit and the power flow constraint. By setting the constraints, the control result obtained by solving the objective function according to the constraint adjustment can ensure the stability of the output power and voltage.

[0075] Among them, the output power constraint of the current photovoltaic power generation unit is used to ensure the stability of the output power of the photovoltaic power generation unit 10, the output constraint of the current photovoltaic power generation unit is used to ensure the stability of the voltage, and the flow constraint is used to ensure the power balance of the node where the current photovoltaic power generation unit is located.

[0076] For further information, see Figure 2 The distribution network includes multiple nodes. The photovoltaic power generation units 10 are distributed on some nodes. Before regulation, some photovoltaic power generation units 10 are selected according to actual needs to form a target set. The regulation algorithm is used to determine the control weight w of the output power of the photovoltaic power generation units 10 in the target set at the current moment. m (t) to control the photovoltaic power generation unit 10. The control weight w of each photovoltaic power generation unit 10 in the target set is m (y) may be different.

[0077] In this way, the control algorithm, computer device, and computer-readable storage medium of the distributed photovoltaic group 100 of the embodiment of the present application can obtain the control weight by determining the objective function according to the control target, and determining the constraint conditions including the power flow constraint, and solving the objective function according to the constraint conditions, thereby realizing the group control of the distributed photovoltaic group 100 to achieve the control target. By controlling the individual photovoltaic power generation unit 10, the group control algorithm is not restricted by the topology of the distributed photovoltaic group 100, which improves the versatility. In addition, the output power constraint, the output constraint of the photovoltaic power generation unit 10, and the power flow constraint are added, and the environmental information is quantified, so that the target power obtained by solving the constraint conditions can optimize the power generation power and keep the voltage stable. In addition, the power control and coordinated optimization of the distributed photovoltaic group can be realized, and the distributed photovoltaic group can be accurately controlled, effectively improving the level of new energy consumption and utilization, and ensuring the safe grid connection of distributed photovoltaics and the safe operation of the distribution network.

[0078] See also Figure 3 In some embodiments, step 01 (determining the objective function according to the control target, target power and reference voltage level of the distributed photovoltaic group 100) includes:

[0079] 011: Determine the power loss according to the total output power value of the distributed photovoltaic group 100 and the target power, where the total output power value is the sum of the output power values ​​of all photovoltaic power generation units 10;

[0080] 012: Determine voltage loss according to the actual voltage level of the photovoltaic power generation unit 10 and the reference voltage level;

[0081] 013: Determine the objective function based on power loss and voltage loss.

[0082] In certain embodiments, the processor can be used to determine the power loss based on the sum of the output power values ​​of the photovoltaic power generation units 10 and the target power, where the sum of the output power values ​​is the sum of the output power values ​​of all the photovoltaic power generation units 10; and to determine the voltage loss based on the actual voltage level of the photovoltaic power generation unit 10 and the reference voltage level; and can also be used to determine the objective function based on the power loss and the voltage loss.

[0083] In some embodiments, the first determination module includes a first determination submodule, a second determination submodule, and a third determination submodule. The first determination submodule can be used to determine the power loss according to the sum of the output power values ​​of the photovoltaic power generation unit 10 and the target power, and the sum of the output power values ​​is the sum of the output power values ​​of all photovoltaic power generation units 10; the second determination submodule can be used to determine the voltage loss according to the actual voltage level of the photovoltaic power generation unit 10 and the reference voltage level; the third determination submodule can be used to determine the objective function according to the power loss and the voltage loss.

[0084] Specifically, to determine whether the output power of the distributed photovoltaic group 100 is close to the target power of regulation, the power loss f can be determined according to the difference between the total output power value of the distributed photovoltaic group 100 and the target power. loss (t). When the output power of the distributed photovoltaic group 100 is closest to the target power of regulation, the power loss f loss (t) minimum. Power loss f loss (t) can be calculated as:

[0085]

[0086] Among them, S M is the node set of all photovoltaic power generation units 10 in the distribution network; m (t) is the output power value of the mth photovoltaic power generation unit 10 at time t; O(t) is the target power at a given time t.

[0087] In order to keep the voltage of distributed photovoltaics stable at a good level, it is necessary to control the deviation between the actual voltage level of the photovoltaic power generation unit 10 and the reference voltage level to be the minimum. Therefore, the voltage loss f is determined according to the actual voltage level of the photovoltaic power generation unit 10 and the reference voltage level.st a ble_i (t), and according to the voltage loss f stable_i (t) Determine the objective function. Voltage loss f st a ble_i (t) can represent the deviation between the actual voltage level and the reference voltage level, and can be calculated as:

[0088]

[0089] Among them, U i (t) is the actual voltage level of the ith photovoltaic power generation unit 10 at time t; the reference voltage of the U0 voltage is level; are the upper and lower limits of the voltage of the i-th photovoltaic power generation unit 10 respectively.

[0090] The total voltage loss of the distributed photovoltaic group 100 can be determined based on the voltage loss of the photovoltaic power generation unit 10:

[0091]

[0092] According to the determined power loss f loss (t) and voltage loss f st a ble_i (t) can get the objective function Min_f(t), and the control weight w can be obtained by solving the objective function according to the agreed adjustment m (t).

[0093] In this way, the power loss is determined according to the total output power values ​​of the photovoltaic power generation unit 10 and the target power, and the voltage loss is determined according to the actual voltage level of the photovoltaic power generation unit 10 and the reference voltage level. Then, the objective function is determined according to the power loss and the voltage loss. This allows the objective function to meet the control objectives, that is, to control the total output power value to be close to the target power and to control the voltage to be stable.

[0094] See also Figure 4 In some embodiments, step 01 (determining the objective function according to the control target, target power and reference voltage level of the distributed photovoltaic group 100) further includes:

[0095] 014: If the photovoltaic power generation unit 10 is under rigid control, determining the output power value of the photovoltaic power generation unit 10 as the product of the first control weight and the output power prediction value, and the first control weight is equal to the first preset value or the second preset value;

[0096] 015: If the photovoltaic power generation unit 10 belongs to flexible control, the output power value of the photovoltaic power generation unit 10 is determined to be the product of the second control weight and the output power prediction value, and the second control weight is greater than the first preset value and less than the second preset value.

[0097] In some embodiments, the processor can be used to determine that the output power value of the photovoltaic power generation unit 10 is the product of a first control weight and an output power prediction value if the photovoltaic power generation unit 10 is under rigid control, and the first control weight is equal to a first preset value or a second preset value; and to determine that the output power value of the photovoltaic power generation unit 10 is the product of a second control weight and an output power prediction value if the photovoltaic power generation unit 10 is under flexible control, and the second control weight is greater than the first preset value and less than the second preset value.

[0098] In some embodiments, the second determination module includes a fourth determination submodule and a fifth determination submodule. The fourth determination submodule can be used to determine that the output power value of the photovoltaic power generation unit 10 is the product of the first control weight and the output power prediction value if the photovoltaic power generation unit 10 is under rigid control, and the first control weight is equal to the first preset value or the second preset value; the fifth determination submodule can be used to determine that the output power value of the photovoltaic power generation unit 10 is the product of the second control weight and the output power prediction value if the photovoltaic power generation unit 10 is under flexible control, and the second control weight is greater than the first preset value and less than the second preset value.

[0099] Specifically, a combination of flexible control and rigid control is adopted for the photovoltaic power generation units 10 of the distributed photovoltaic group 100, that is, flexible control is adopted for some photovoltaic power generation units 10, and rigid control is adopted for some photovoltaic power generation units 10. The control weight of the photovoltaic power generation unit 10 under flexible control has a value range of (0, 1), and the control weight of the first photovoltaic power generation under rigid control has a value range of {0, 1}. The output power of the photovoltaic power generation unit 10 is predicted to obtain a power prediction value. The output power value of the photovoltaic power generation unit 10 can be determined based on the power prediction value and the control weight, and the calculation method of the output power value includes:

[0100]

[0101] Among them, S A , S B are a set of photovoltaic power generation units 10 respectively for flexible control and rigid control, and S A ∪S B =S M ; P′ m (t) is the power prediction value of the m-th photovoltaic power generation unit 10 at time t; w m (t) is the control weight of each photovoltaic power generation unit 10.

[0102] Therefore, the power loss f loss (t) can be calculated as:

[0103]

[0104] In this way, the output power of the photovoltaic power generation unit 10 at the current moment can be determined according to the control weight and the power prediction value of the photovoltaic power generation unit 10 .

[0105] See also Figure 5 In some embodiments, step 013 (determining the objective function based on power loss and voltage loss) includes:

[0106] 0131: The objective function is determined based on the sum of the square of the power loss and the square of the voltage loss.

[0107] In certain embodiments, the processor may be configured to determine the objective function based on a sum of the square of the power loss and the square of the voltage loss.

[0108] In some embodiments, the third determination submodule includes a first determination unit, and the first determination unit can be used to determine the objective function according to the sum of the square of the power loss and the square of the voltage loss.

[0109] Specifically, in order to unify the dimensions and mathematical forms of the objective functions, the power loss f loss (t) and voltage loss f stable_i (t) is converted into a quadratic form, that is, it can be calculated based on the power loss f loss The square of (t) and voltage loss f stable_i The sum of the squares of (t) determines the objective function Min_f(t):

[0110]

[0111] Among them, w m (t) is the control weight of the mth photovoltaic power generation unit 10 at time t; P′ m (t) is the power prediction value of the m-th photovoltaic power generation unit 10 at time t.

[0112] In this way, the objective function can be determined based on the power loss and voltage loss, so that the control weights obtained by solving the objective function can meet the requirements of the regulation target.

[0113] See also Figure 6 In some embodiments, step 02 (determining constraints) includes:

[0114] 021: Determine an output power constraint according to the predicted power generation power of the current photovoltaic power generation unit 10, where the output power constraint includes: the output power of the current photovoltaic power generation unit 10 is less than the predicted power generation power.

[0115] In some embodiments, the processor may be used to determine an output power constraint based on the predicted power generation of the current photovoltaic power generation unit 10 , where the output power constraint includes: the output power of the current photovoltaic power generation unit 10 is less than the predicted power generation.

[0116] In some embodiments, the second determination module includes a sixth determination submodule, which can be used to determine the output power constraint based on the predicted power generation power of the current photovoltaic power generation unit 10, and the output power constraint includes: the output power of the current photovoltaic power generation unit 10 is less than the predicted power generation power.

[0117] Specifically, an output power constraint is set for the current photovoltaic power generation unit, requiring that the output power of the current photovoltaic power generation unit is greater than 0 and less than the output power upper limit, and the output power upper limit is the predicted power value. The power constraints include:

[0118]

[0119] Among them, P m (t) is the output power of the m-th photovoltaic power generation unit 10 at time t, which must be kept below the maximum output power; is the output power upper limit of the m-th photovoltaic power generation unit 10 at time t, that is, the predicted power generation power.

[0120] In this way, by setting the power constraint, the obtained control weight can meet the demand for power stability.

[0121] See also Figure 7 In some embodiments, step 02 (determining constraints) includes:

[0122] 022: Determine the current output constraint of the photovoltaic power generation unit 10 according to the voltage upper limit value and the voltage lower limit value, and the current output constraint of the photovoltaic power generation unit 10 includes: the current voltage of the photovoltaic power generation unit 10 is greater than the voltage lower limit value and less than the voltage upper limit value.

[0123] In some embodiments, the processor can be used to determine the current output constraint of the photovoltaic power generation unit 10 based on the voltage upper limit value and the voltage lower limit value. The current output constraint of the photovoltaic power generation unit 10 includes: the voltage of the current photovoltaic power generation unit 10 is greater than the voltage lower limit value and less than the voltage upper limit value.

[0124] In some embodiments, the second determination module includes a seventh determination submodule, which can be used to determine the current output constraint of the photovoltaic power generation unit 10 based on the voltage upper limit value and the voltage lower limit value. The current output constraint of the photovoltaic power generation unit 10 includes: the voltage of the current photovoltaic power generation unit 10 is greater than the voltage lower limit value and less than the voltage upper limit value.

[0125] Specifically, the output constraint is set for the current photovoltaic power generation unit to control the voltage of the current photovoltaic power generation unit to be greater than the lower voltage limit and less than the upper voltage limit. That is, the output constraint of the current photovoltaic power generation unit includes:

[0126]

[0127] Among them, U i (t) is the voltage value of the i-th photovoltaic power generation unit at time 10t; is the voltage lower limit value of the i-th photovoltaic power generation unit 10; is the voltage upper limit value of the i-th photovoltaic power generation unit 10.

[0128] In this way, by setting the output constraint, the voltage of the current photovoltaic power generation unit can be controlled to be greater than the voltage lower limit value and less than the voltage upper limit value, so as to maintain the voltage between the voltage upper limit value and the voltage lower limit value.

[0129] See also Figure 8 In some embodiments, step 02 (determining constraints) includes:

[0130] 023: Determine the power flow constraints based on Kirchhoff’s voltage-current law and the system active and reactive power balance formula.

[0131] In certain embodiments, the processor may be configured to determine the power flow constraint based on Kirchhoff's voltage and current laws and a system active and reactive power balance formula.

[0132] In some embodiments, the second determination module includes an eighth determination submodule, and the eighth determination submodule can be used to determine the power flow constraint according to Kirchhoff's voltage and current law and the system active and reactive power balance formula.

[0133] Specifically, to meet the power flow constraints of the distributed photovoltaic group 100 under the distribution network, the power flow constraints can be determined by Kirchhoff's voltage and current law and the system active and reactive balance formula, and the power flow constraints can include active power flow constraints, reactive power flow constraints and power balance power flow constraints. The voltage value at the node in the branch can be determined according to Kirchhoff's voltage and current law.

[0134] The system active and reactive power balance formula includes the active power balance formula and the reactive power balance formula. The active power balance formula refers to the sum of all active powers generated at the node (∑P G ) is equal to the sum of the active power consumed (∑P L ) and the sum of active power loss (ΔP Σ ), that is, ∑P G =∑P L +ΔP Σ .

[0135] The reactive power balance formula refers to the sum of all reactive powers generated at the node (∑Q G ) is equal to the sum of the reactive power consumed (∑Q L ) and the sum of reactive power loss (ΔQ Σ ), that is, ∑Q G=∑Q L +ΔQ Σ .

[0136] In this way, the power flow constraint can be determined according to Kirchhoff's voltage-current law and the system active and reactive power balance formula, so that the distributed photovoltaic group 100 meets the power flow balance requirement.

[0137] See also Fig. 9 In some embodiments, the power flow constraint includes an active power flow constraint, the distributed photovoltaic group 100 includes a first node and a second node, and step 02 (determining the constraint condition) includes:

[0138] 024: Determine the active power flow constraint according to the active power flow between the first node and the second node of the distributed photovoltaic group 100, the reactive power flow between the first node and the second node, the voltage value of the photovoltaic power generation unit 10 at the first node, the resistance value between the first node and the second node, and the active power injected into the second node.

[0139] In some embodiments, the processor can be used to determine the active power flow constraint based on the active power flow between the first node and the second node of the distributed photovoltaic group 100, the reactive power flow between the first node and the second node, the voltage value of the photovoltaic power generation unit 10 at the first node, the resistance value between the first node and the second node, and the active power injected into the second node.

[0140] In some embodiments, the second determination module includes a ninth determination submodule, which can be used to determine the active power flow constraint based on the active power flow between the first node and the second node of the distributed photovoltaic group 100, the reactive power flow between the first node and the second node, the voltage value of the photovoltaic power generation unit 10 at the first node, the resistance value between the first node and the second node, and the active power injected into the second node.

[0141] Specifically, the first node can be Fig.10 The second node can be node i in the figure. When the distributed photovoltaic is in the distribution network, for node j ( S N The active power flow constraints of (being the set of all nodes in all distribution networks) include:

[0142]

[0143] Among them, P ij (t) is the active power flow between node i and node j at time t; Q ij (t) is the reactive power flow between node i and node j at time t; U i (t) is the voltage value of the i-th user at time t; r ij is the resistance value between node i and node j; Pj (t) is the active power injected by node j at time t; up(j) is the set of branch headend nodes whose branch end is node j; dn(j) is the set of branch end nodes whose branch headend is node j.

[0144] In the calculation formula of active power flow constraint, the left side of the equal sign represents the total active power flowing into node j minus the line loss, and the right side of the equal sign represents the active power flowing out of node j and the power injection of node j itself. Active power flow constraint ensures the active power balance of node j.

[0145] In this way, active power balance at the node can be ensured by determining active power flow constraints.

[0146] See also Fig.11 In some embodiments, the power flow constraint includes a reactive power flow constraint, the distributed photovoltaic group 100 includes a third node and a fourth node, and step 02 (determining the constraint condition) includes:

[0147] 025: Determine the reactive power flow constraint according to the active power flow between the third node and the fourth node of the distributed photovoltaic group 100, the reactive power flow between the third node and the fourth node, the voltage value of the photovoltaic power generation unit 10 at the third node, the reactance value between the third node and the fourth node, and the active power injected into the fourth node.

[0148] In some embodiments, the processor can be used to determine the reactive power flow constraint based on the active power flow between the third node and the fourth node of the distributed photovoltaic group 100, the reactive power flow between the third node and the fourth node, the voltage value of the photovoltaic power generation unit 10 at the third node, the reactance value between the third node and the fourth node, and the active power injected into the fourth node.

[0149] In some embodiments, the second determination module includes a tenth determination submodule, which can be used to determine the reactive power flow constraint based on the active power flow between the third node and the fourth node of the distributed photovoltaic group 100, the reactive power flow between the third node and the fourth node, the voltage value of the photovoltaic power generation unit 10 at the third node, the reactance value between the third node and the fourth node, and the active power injected into the fourth node.

[0150] Specifically, the third node can be Fig.10 The fourth node can be Fig.10 When the distributed photovoltaic is in the distribution network, for node j ( S N The active power flow constraints of (being the set of all nodes in all distribution networks) include:

[0151]

[0152] Among them, P ij (t) is the active power flow between node i and node j at time t; Q ij (t) is the reactive power flow between node i and node j at time t; U i (t) is the voltage value of the ith photovoltaic power generation unit at time 10t; x ij is the reactance value between node i and node j; Q j (t) is the reactive power injected by node j at time t; up(j) is the set of branch headend nodes whose branch end is node j; dn(j) is the set of branch end nodes whose branch headend is node j.

[0153] In the equation for active power flow constraint, Q on the left side of the equal sign is ij (t) represents the comprehensive reactive power flow from node i to node j, Represents the reactive power loss on the line between node i and node j.

[0154] Q on the right side of the equal sign jk (t) represents the reactive power flow from node j to downstream node k. j (t) represents the reactive power injection amount of node j itself. The reactive power flow constraint ensures the balance of reactive power at node j.

[0155] In this way, reactive power balance at the node can be ensured by determining reactive power flow constraints.

[0156] See also Fig.12 In some embodiments, the power flow constraint includes a power balance power flow constraint, the distributed photovoltaic group 100 includes a fifth node and a sixth node, and step 02 (determining the constraint condition) includes:

[0157] 026: Determine the power balance flow constraint according to the voltage value of the photovoltaic power generation unit 10 at the fifth node, the voltage value of the photovoltaic power generation unit 10 at the sixth node, the resistance value between the fifth node and the sixth node, the active flow between the fifth node and the sixth node, the reactive flow between the fifth node and the sixth node, and the reactance and impedance between the fifth node and the sixth node.

[0158] In some embodiments, the processor can be used to determine the power balance flow constraint based on the voltage value of the photovoltaic power generation unit 10 at the fifth node, the voltage value of the photovoltaic power generation unit 10 at the sixth node, the resistance value between the fifth node and the sixth node, the active flow between the fifth node and the sixth node, the reactive flow between the fifth node and the sixth node, and the reactance and impedance between the fifth node and the sixth node.

[0159] In some embodiments, the second determination module includes an eleventh determination submodule, which can be used to determine the power balance flow constraint based on the voltage value of the photovoltaic power generation unit 10 at the fifth node, the voltage value of the photovoltaic power generation unit 10 at the sixth node, the resistance value between the fifth node and the sixth node, the active flow between the fifth node and the sixth node, the reactive flow between the fifth node and the sixth node, and the reactance and impedance between the fifth node and the sixth node.

[0160] Specifically, the fifth node can be Fig.10 The sixth node can be Fig.10 When the distributed photovoltaic is in the distribution network, for branch ij ( S L The active power flow constraints of (the set of all branches in all distribution networks) include:

[0161]

[0162] Among them, U i (t) is the voltage value of the photovoltaic power generation unit 10t at the node i; U j (t) is the voltage value of the photovoltaic power generation unit 10t at the node j; r ij is the resistance between node i and node j; P ij (t) is the active power flow between node i and node j at time t; x ij is the reactance between node i and node j; Q ij (t) is the reactive power flow between node i and node j at time t; I ij is the impedance between node i and node j.

[0163] For active power flow constraints, the left side of the equal sign (U i (t)) 2 -(U j (t)) 2 It represents the square difference of the voltage values ​​at nodes i and j. The right side of the equal sign represents the active power loss caused by the line resistance, the reactive power loss caused by the line reactance, and the total power loss on the line. The active power flow constraints are determined to ensure that the power balance relationship can be maintained during the power transmission process.

[0164] In this way, by determining the active power flow constraint, it is possible to ensure that the power balance relationship can be maintained during the power transmission process.

[0165] See also Fig.13 In some embodiments, step 03 (determining the control weight of the current photovoltaic power generation unit 10 according to the constraint conditions and the objective function) includes:

[0166] 031: Use particle swarm algorithm to determine control weights based on constraints and objective functions.

[0167] In some embodiments, the processor may be configured to use a particle swarm algorithm to determine the control weights based on the constraints and the objective function.

[0168] In some embodiments, the second determination module includes a twelfth determination submodule, and the twelfth determination submodule can be used to determine the control weight according to the constraint conditions and the objective function using a particle swarm algorithm.

[0169] Specifically, the algorithm principle of Particle Swarm Optimization (PSO) is relatively simple and does not require derivatives or gradient-based information, so it is suitable for complex optimization problems that are not easy to differentiate or non-convex. Compared with methods such as genetic algorithms (GA), PSO does not involve complex operations such as crossover and mutation, but iterates through simple speed and position updates. Since the update rules of PSO are simple and easy to implement, the code implementation and adjustment are also relatively easy, so it has high flexibility for application scenarios, is easy to understand, implement and quickly calculate, and can be used to solve the objective function according to constraint adjustment to determine the control weight w m (t).

[0170] In addition, algorithms such as Genetic Algorithm (GA) and Simulated Annealing (SA) can also be used to solve the objective function according to constraint adjustments.

[0171] In this way, the particle swarm algorithm is used to solve the objective function according to the constraints, and the control weight can be quickly obtained.

[0172] An embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of implementing the control algorithm of any of the above embodiments can be understood, and the computer program includes computer program code.

[0173] The computer program code may be in source code form, object code form, executable file or some intermediate form, etc. Computer readable storage media may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, mobile hard disks, magnetic disks, optical disks, computer memories, read-only memories (ROM), random access memories (RAM), and software distribution media, etc.

[0174] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0175] In addition, the term "connection" should be understood in a broad sense, for example, it can include fixed connection, detachable connection, or integral connection; it can include direct connection, indirect connection through an intermediate medium, and internal communication between two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0176] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0177] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0178] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A control algorithm for a distributed photovoltaic group, characterized in that: The distributed photovoltaic group includes a plurality of photovoltaic power generation units arranged in a distributed manner, and the control algorithm includes: Determining an objective function according to a control target, a target power and a reference voltage level of the distributed photovoltaic group; Determining constraint conditions, wherein the constraint conditions include an output power constraint of a current photovoltaic power generation unit, an output constraint of a current photovoltaic power generation unit, and a power flow constraint; Determining a control weight of the current photovoltaic power generation unit according to the constraint condition and the objective function; The output power of the current photovoltaic power generation unit is controlled according to the control weight to achieve regulation of the distributed photovoltaic group.

2. The control algorithm according to claim 1, characterized in that: The determining of the objective function according to the regulation target, target power and reference voltage level of the distributed photovoltaic group includes: Determine the power loss according to the total output power value of the distributed photovoltaic group and the target power, the total output power value is the sum of the output power values ​​of all the photovoltaic power generation units; determining a voltage loss according to an actual voltage level of the photovoltaic power generation unit and a reference voltage level; The objective function is determined according to the power loss and the voltage loss.

3. The control algorithm according to claim 2, characterized in that: The determining of the objective function according to the regulation target, target power and reference voltage level of the distributed photovoltaic group further includes: If the photovoltaic power generation unit is under rigid control, determining an output power value of the photovoltaic power generation unit as a product of a first control weight and an output power prediction value, wherein the first control weight is equal to a first preset value or a second preset value; If the photovoltaic power generation unit belongs to flexible control, the output power value of the photovoltaic power generation unit is determined to be the product of a second control weight and an output power prediction value, and the second control weight is greater than the first preset value and less than the second preset value.

4. The control algorithm according to claim 2, characterized in that: The determining the objective function according to the power loss and the voltage loss comprises: The objective function is determined according to a sum of the square of the power loss and the square of the voltage loss.

5. The control algorithm according to claim 1, characterized in that: The determining constraint conditions include: The output power constraint is determined according to the predicted power generation power of the current photovoltaic power generation unit, and the output power constraint includes: the output power of the current photovoltaic power generation unit is less than the predicted power generation power.

6. The control algorithm according to claim 1, characterized in that: The determining constraint conditions include: The current photovoltaic power generation unit output constraint is determined according to the voltage upper limit value and the voltage lower limit value, and the current photovoltaic power generation unit output constraint includes: the voltage of the current photovoltaic power generation unit is greater than the voltage lower limit value and less than the voltage upper limit value.

7. The control algorithm according to claim 1, characterized in that: The determining of the constraint conditions comprises: The power flow constraint is determined according to Kirchhoff's voltage-current law and the system active and reactive power balance formula.

8. The control algorithm according to claim 1, characterized in that: The power flow constraint includes an active power flow constraint, the distributed photovoltaic group includes a first node and a second node, and the determining constraint condition includes: The active power flow constraint is determined according to the active power flow between the first node and the second node of the distributed photovoltaic group, the reactive power flow between the first node and the second node, the voltage value of the photovoltaic power generation unit at the first node, the resistance value between the first node and the second node, and the active power injected into the second node.

9. The control algorithm according to claim 1, characterized in that: The power flow constraint includes a reactive power flow constraint, the distributed photovoltaic group includes a third node and a fourth node, and the determining constraint condition includes: The reactive power flow constraint is determined according to the active power flow between the third node and the fourth node of the distributed photovoltaic group, the reactive power flow between the third node and the fourth node, the voltage value of the photovoltaic power generation unit at the third node, the reactance value between the third node and the fourth node, and the active power injected by the fourth node.

10. The control algorithm according to claim 1, characterized in that: The power flow constraint includes a power balance power flow constraint, the distributed photovoltaic group includes a fifth node and a sixth node, and the determining constraint condition includes: The power balance flow constraint is determined according to the voltage value of the photovoltaic power generation unit at the fifth node, the voltage value of the photovoltaic power generation unit at the sixth node, the resistance value between the fifth node and the sixth node, the active flow between the fifth node and the sixth node, the reactive flow between the fifth node and the sixth node, and the reactance and impedance between the fifth node and the sixth node.

11. The control algorithm according to claim 1, characterized in that: The step of determining the control weight of the current photovoltaic power generation unit according to the constraint condition and the objective function includes: The control weight is determined according to the constraint conditions and the objective function using a particle swarm algorithm.

12. A computer device, characterized in that: The computer device includes one or more processors and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the control algorithm described in any one of claims 1 to 11 are implemented.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the control algorithm described in any one of claims 1 to 11 are implemented.

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